In an electric distribution network or grid, computational tasks, such as analyzing and storing data, are typically centralized and performed at the head-end system. The data collected by the endpoints is sent to the head-end system and the head-end system handles the analysis and decision-making. The head-end system may use the data to make decisions, control resources within the electric distribution network, or optimize the use of resources. However, there are some disadvantages to relying on the head-end system to perform the analysis, including the delay between the time the data is collected by an endpoint and the time the data is analyzed by the head-end system and the amount of communication bandwidth required to send the data to the head-end system.
By dynamically distributing computational tasks and other services, these issues can be addressed, and advantages can be obtained. Advantages may include obtaining results with less delay, using fewer head-end processing resources when scaling, using less network bandwidth, minimizing the overall cost, improving fault tolerance, and improving scalability.